Expected by Whom? A Shooter and Goaltender Skill-adjusted Expected Goals Model for the NHL

Abstract: This study outlines a light gradient boosted model aimed at predicting shot outcomes in the NHL. The model uses the NHL's spatiotemporal data to account for both the skill of shooters and goaltenders. This approach involves isolating and engineering features for different aspects of shooter and goaltender skill. These aspects include the overall skill, the locational skill, which is engineered using a shot binning technique previously outlined by Shuckers and Curro, and the situational skill, which is engineered using Gower distance. Three separate datasets were created based on the skill of the shooter and goaltender. For each, a baseline model was created in order to compare and contrast its performance with the skill-adjusted model. The results seen in this study show performance increases for the skill-adjusted model over the baseline model in log loss, brier scores, and area under the ROC curve. These performance increases have a high of 5\% and outperform previous works, which have attempted to account only for player skill. This highlights the importance of accounting for both player and goaltender skill, while also accounting for different aspects of their skill. In future works, a skill-adjusted expected goals model could benefit models interested in predicting other aspects of the game, such as scoring leaders or individual game outcomes.